CN116704775A - Mixed traffic flow traffic capacity calculation method considering intelligent network bus - Google Patents
Mixed traffic flow traffic capacity calculation method considering intelligent network bus Download PDFInfo
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Abstract
The invention discloses a mixed traffic flow traffic capacity calculation method considering an intelligent network bus, which is used for exploring the characteristics and the differences of micro driving behaviors such as following, lane changing, interaction and the like of the intelligent network bus, the intelligent network private bus and the manual driving bus after accurately describing the mixed traffic flow average traffic time, the average traffic density of the mixed traffic flow, the traffic quantity of the mixed traffic flow and the like of the mixed traffic flow and considering the driving delay time; the organic combination of the micro driving behavior and the macro traffic characteristics of the vehicle is realized; meanwhile, the density of the ghost is introduced to describe microscopic behaviors such as vehicle speed change, lane change, bus stop service and the like from the angle of time-space domain occupation, the limitation of lacking a microscopic driving behavior system description method when the traffic capacity of the mixed traffic flow is calculated is broken through, and the method can be effectively applied to various traffic states and development conditions of different intelligent networking technologies.
Description
Technical Field
The invention relates to the field of Internet information technology application services, in particular to a method for calculating the traffic capacity of a hybrid traffic flow of an intelligent network bus.
Background
The intelligent network vehicle is an emerging product of the combination of the automobile industry and new generation information technology, artificial intelligence, big data and other technologies, and can greatly improve the running efficiency and safety of the vehicle. With the development of new generation internet information technology, intelligent network vehicles are rushed into traditional traffic networks, heterogeneous traffic flows of intelligent network vehicles and manual driving vehicles mixed on urban roads exist for a long time, and the interaction influence among vehicles is obviously changed, so that the overall traffic flow characteristics are influenced. Meanwhile, due to the fact that urban buses have fixed operation rules and natural infrastructure conditions, the urban buses become application scenes of the new generation of information technology and the intelligent network technology, and intelligent network buses, intelligent network buses and novel mixed traffic flows of manual driving vehicles become necessary stages of urban traffic development. The bus is constrained by conditions such as inbound service characteristics, fixed lines, bus lanes and the like, a plurality of forced lane changing behaviors exist in the process of entering and exiting the bus station and passing through an intersection, frequent interaction exists between the bus and a social vehicle, and whether the interaction between the complex bus driving behaviors and heterogeneous vehicles can cause the distinct characteristics of mixed traffic flows is not clear. The calculation method for the traffic capacity of the traditional traffic flow cannot be directly applied to the mixed traffic flow, and the traffic state cannot be accurately estimated by means of the information technology, and mainly has the following problems:
the existing research on traffic flows is mostly aimed at the mixed traffic flows consisting of intelligent network private cars and manual driving vehicles, the important influence of the intelligent network buses on traffic flow characteristics is ignored, and a mixed traffic flow system description method considering bus operation rules is lacking; the existing research on traffic flow mostly reflects the difference of different vehicles in following and lane changing behaviors by changing model parameters, and has lower calculation precision in terms of describing the difference of intelligent network buses, intelligent network private buses, manual driving vehicles in reaction time, headway, acceptable lane changing distance and the like.
Disclosure of Invention
The invention provides a method for calculating the traffic capacity of a mixed traffic flow of an intelligent network bus, which aims to overcome the technical problems.
In order to achieve the above object, the technical scheme of the present invention is as follows:
a method for calculating the traffic capacity of a mixed traffic flow of an intelligent network bus comprises the following steps:
s1: acquiring average headway of mixed traffic flows under different intelligent network coupling permeability;
the mixed traffic flow consists of a manual driving vehicle, an intelligent network private car and an intelligent network bus; the intelligent network private car and the intelligent network bus have a smaller time-to-time distance than that of a manual driving car;
the intelligent network connection permeability refers to the proportion of the number of intelligent network connection private cars to the total number of vehicles in the mixed traffic flow;
s2: acquiring the flow of an intelligent network bus running from an intersection to a bus station in the [ t, t+delta t ] time period;
s3: acquiring the vehicle speed delay time in the running delay time of the mixed traffic flow, and acquiring the signal lamp delay time in the running delay time of the mixed traffic flow according to the average headway of the mixed traffic flow so as to acquire the uniform traffic time of the mixed traffic flow taking the running delay time into consideration;
s4: according to the ghost density theory, obtaining the average traffic density of the mixed traffic flow;
s5: and acquiring the traffic of the mixed traffic flow according to the traffic of the intelligent network bus, the equal traffic time of the mixed traffic flow and the average traffic density of the mixed traffic flow so as to evaluate the traffic capacity of the mixed traffic flow.
Further, in the step S1, the average headway under different intelligent network connection permeability is obtained as follows:
in the formula ,representing the average headway of the mixed traffic flow; zeta type toy a Indicating the permeability of intelligent network connection; n is n m Representing a total number of vehicles in the mixed traffic stream; n is n b Indicating the number of intelligent network buses in the mixed traffic flow, and xi a n m Representing the number of intelligent network private cars in the mixed traffic flow; h is a hv-hv Representing a headway between the manually driven vehicle and the manually driven vehicle; h is a hv-cv Representing the headway between a manual driving vehicle and an intelligent network private car; h is a hv-cb Representing the time interval between a manual driving vehicle and an intelligent network bus; h is a cv-hv Representing the headway between the intelligent network private car and the manual driving car; h is a cv-cv Representing the time interval between the private car of the intelligent network and the private car of the intelligent network; h is a cb-hv And the time interval between the intelligent network bus and the manual driving vehicle is represented.
Further, in the step S2, the flow of the intelligent network bus is obtained as follows:
in the formula ,represents [ t, t+Δt ]]The flow of the intelligent network bus output by the bus station in the time period; n (N) b Representing the number of intelligent network buses in a bus station; p is p t (N b Not less than 2) represents [ t, t+Δt ]]Probability that more than two intelligent network buses stop at a bus stop in a time period; p is p t (N b < 2) represents [ t, t+Δt ]]Probability of stopping less than two intelligent network buses in a time period bus station; />Represents [ t, t+Δt ]]The total flow of the intelligent network bus when more than two intelligent network buses are parked at the bus station in the time period; />Represents [ t, t+Δt ]]The total flow of the intelligent network bus when the bus stops at a time period and is not more than two intelligent network buses; />Represents [ t, t+Δt ]]The total service time of the intelligent network bus at the bus station in the time period; />Represents [ t, t+Δt ]]The running time of the intelligent network bus from the intersection to the bus station in the time period; t represents the current time; Δt represents a unit time step;
wherein ,
in the formula ,representing the service strength of a bus station; />Representing the maximum service intensity of a bus station; />Representing the arrival rate of the intelligent network bus; />Representing the service rate of a bus stop->p t (N b =0) indicates the probability that no intelligent network bus is parked in the bus station at time t; j represents the number of intelligent network buses parked in the bus station; k represents the number of intelligent network buses that may reach a bus station.
Further, the signal lamp delay time is obtained as follows:
in the formula ,represents [ t, t+Δt ]]The driving delay time caused by the signal lamp in the time period; r represents the red light duration of the signal light; />Represents [ t, t+Δt ]]The generation speed of the shock wave caused by the red light in the time period; />Represents [ t, t+Δt ]]The dissipation speed of the shock wave after the red light is finished in the time period; c m Representing the maximum flow of the mixed traffic flow at the intersection; />Represents [ y, t+Δt ]]The arrival rate of the mixed traffic flow at the intersection in the time period comprises intelligent network private cars, manual driving cars, left-turning and straight-going intelligent network buses; 1-p RB The proportion of left turn and straight travel of the intelligent network bus is represented; n represents the number of lanes of the road where the mixed traffic flow is located; k (k) j Representing a critical congestion density; />Representing [ t-Deltat, t]The average running speed of the mixed traffic flow in the time period; />Represents [ t, t+Δt ]]The arrival rate of the social vehicles in the time period comprises intelligent network private cars and manual driving vehicles; />And the arrival rate of the intelligent network bus is represented.
Further, the vehicle speed delay time is obtained as follows:
in the formula ,representing the passing time increasing rate caused by the speed change of the intelligent network private car and the intelligent network bus; l (L) b Representing the total length of the distance travelled by the bus station to the intersection; l (L) l Representing the total length of the road section where the bus station is located; gamma ray m Representing the proportion of vehicles affected by the speed change of the intelligent network private car and the intelligent network bus; />Represents [ t, t+Δt ]]And the expected running speed of the intelligent network private car and the intelligent network bus in the time period.
Further, considering the average traffic flow time T of the mixed traffic flow after the running delay time t The acquisition is as follows:
further, in the step S4, the average traffic density of the mixed traffic flow is obtained as follows:
in the formula ,represents [ t, t+Δt ]]Traffic density of the mixed traffic stream over a period of time; />Representing [ t-Deltat, t]Total number of vehicles in the mixed traffic stream over a period of time; />Represents [ t, t+Δt ]]The number of ghost vehicles generated in the time period; />Represents [ t, t+Δt ]]Ghost vehicles generated by speed change of intelligent network private vehicles and intelligent network buses in a time period;represents [ t, t+Δt ]]Ghost vehicles generated by intelligent network bus station parking in the time period; />Represents [ t, t+Δt ]]Ghost vehicles generated by forced lane change of intelligent network bus and random lane change of manual driving vehicles in the time period;represents [ t, t+Δt ]]The lane changing distance required by the intelligent network bus in the time period; />Represents [ t, t+Δt ]]Manually driven vehicle during time periodThe required lane change distance; />Represents [ t, t+Δt ]]The intelligent network bus stops waiting for passengers to get on or off the bus within the time period; n (N) LC Representing the number of lane change vehicles; t is t LC Indicating the channel change duration; p is p RB The right turn proportion of the intelligent network bus at the intersection is shown; p is p LB Representing the left-turning proportion of the intelligent network bus at the intersection; p is p SB Representing the proportion of the intelligent network bus in straight running at the intersection; n represents the number of lanes of the road where the mixed traffic flow is located, t blc Indicating the time required by lane change of the intelligent network bus; t is t hlc The time required by the manual driving of the vehicle to change the road is represented; l (L) b Representing the length of an intelligent network bus body; l (L) h Representing the length of the body of the manually driven vehicle;
in the formula ,representing the following distance of the intelligent network bus; />Representing the distance required by acceleration and deceleration of the vehicle in the course of lane change; />Representing the distance required by speed change in the lane change process of the intelligent network bus; t is t b Representing the following time of a manually driven vehicle; />Representing [ t-Deltat, t]Average travel speed of the mixed traffic stream over a period of time; />Representing a following distance of the manually driven vehicle; v l Representing a following speed of the manually driven vehicle; />Representing the distance required by speed change in the course of changing the lane of the manual driving vehicle; d, d f,b Representing the safe following distance of the intelligent network bus; d, d f,h Representing a safe following distance of the manually driven vehicle; τ f,b Indicating the reaction time required by the intelligent network bus; τ f,h Representing the reaction time required by manual driving of the vehicle; />Represents [ t, t+Δt ]]And the expected running speeds of the intelligent Internet-connected buses and the intelligent Internet-connected private buses are within a time period.
Further, the traffic of the mixed traffic flow is obtained as follows:
in the formula ：qt Representing the overall traffic of the mixed traffic flow;representing the traffic of social vehicles except intelligent network buses.
The beneficial effects are that: according to the method, characteristics and differences of micro driving behaviors of the intelligent network bus, the intelligent network private car and the manual driving car in following, lane changing, interaction and the like are explored after the mixed traffic flow average traffic time, the mixed traffic flow average traffic density, the mixed traffic flow traffic quantity and the like of the mixed traffic flow considering the running delay time are accurately described; fully considering the difference of acceptable headway of heterogeneous vehicles, and acquiring the average headway of the mixed traffic flow under the condition of different intelligent network private vehicles; considering the influence of the speed of the intelligent network bus entering and exiting to the social vehicle (intelligent network private vehicle, manual driving vehicle) and the driving delay time caused by the intersection signal lamp, obtaining the uniform traffic time of the mixed traffic flow, and realizing the organic combination of the micro driving behavior and the macroscopic traffic characteristics of the vehicle; meanwhile, the ghost density is introduced, microscopic behaviors such as vehicle speed change, lane change, bus stop service and the like are described from the perspective of time-space domain occupation, and the limitation of lacking a microscopic driving behavior system description method when the traffic flow capacity of the mixed traffic is calculated is broken through. The invention fully considers the difference of heterogeneous vehicle driving behaviors and the complexity of the interaction process, and can be effectively applicable to various traffic states and different intelligent networking technology development conditions.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are needed in the embodiments or the description of the prior art will be briefly described below, it will be obvious that the drawings in the following description are some embodiments of the present invention, and that other drawings can be obtained according to these drawings without inventive effort to a person skilled in the art.
FIG. 1 is a flow chart of a method for calculating the traffic capacity of a mixed traffic flow of an intelligent network bus;
FIG. 2 is a comparison diagram of simulation result error analysis in an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The embodiment provides a method for calculating the traffic capacity of a mixed traffic flow of an intelligent network bus, as shown in fig. 1, comprising the following steps:
s1: acquiring average headway of mixed traffic flows under different intelligent network coupling permeability;
the mixed traffic flow comprises three vehicles, namely a manual driving vehicle, an intelligent network private vehicle and an intelligent network bus; the intelligent network private car refers to a private car carrying intelligent network technology, and the intelligent network public bus refers to a bus carrying intelligent network technology; the intelligent network private car and the intelligent network bus have the function of guiding the running speed of a motorcade, and the headway is smaller than that of a manual driving car; wherein, by intelligent network allies oneself with private car and intelligent network allies oneself with the bus and can cooperate the driving just can form different motorcades.
The intelligent network connection permeability refers to the proportion of the number of intelligent network connection private cars to the total number of vehicles in the mixed traffic flow, wherein the larger the number of intelligent network connection private cars is, the higher the information technology development level is.
Specifically, the intelligent network vehicle-connected permeability comprises less than 50%, equal to 50% and more than 50%, when the intelligent network vehicle-connected permeability is less than 50%, most vehicles have no communication capability, and a vehicle team guided by intelligent network private vehicles cannot be formed; when the permeability of the intelligent network connected vehicles is 50%, heterogeneous vehicle fleets consisting of intelligent network connected private vehicles and manual driving vehicles are commonly existing in the mixed traffic flow; when the permeability of the intelligent network bus is greater than 50%, all the motorcades in the mixed traffic flow are motorcades guided by the intelligent network bus or the intelligent network private car;
preferably, in the step S1, the average headway under different intelligent network vehicle-connected permeabilities is obtained as follows:
in the formula ,representing the average headway of the mixed traffic flow; zeta type toy a Indicating the permeability of intelligent network connection; n is n m Representing a total number of vehicles in the mixed traffic stream; n is n b Indicating the number of intelligent network buses in the mixed traffic flow, and xi a n m Representing the number of intelligent network private cars in the mixed traffic flow; h is a hv-hv Representing a headway between the manually driven vehicle and the manually driven vehicle; h is a hv-cv Representing the headway between a manual driving vehicle and an intelligent network private car; h is a hv-cb Representing the time interval between a manual driving vehicle and an intelligent network bus; h is a cv-hv Representing the headway between the intelligent network private car and the manual driving car; h is a cv-cv Representing the time interval between the private car of the intelligent network and the private car of the intelligent network; h is a cb-hv Representing the time interval between the intelligent network bus and the manual driving vehicle;
s2: acquiring the flow of an intelligent network bus running from an intersection to a bus station in the [ t, t+delta t ] time period;
wherein, because each bus station can stop two buses, namely, two berths (the bay type bus station can have three berths) are included, and the two berths are independently served, each berth can be regarded as an independent service desk in the queuing system, and further, the process of entering and exiting the intelligent network bus station can be described as a queuing process at the two berths;
preferably, in the step S2, the flow of the intelligent network bus is obtained as follows:
in the formula ,represents [ t, t+Δt ]]The flow of the intelligent network bus output by the bus station in the time period; n (N) b Representing the number of intelligent network buses in a bus station; p is p t (N b Not less than 2) represents [ t, t+Δt ]]Probability that more than two intelligent network buses stop at a bus stop in a time period; p is p t (N b < 2) represents [ t, t+Δt ]]Probability of stopping less than two intelligent network buses in a time period bus station; />Represents [ t, t+Δt ]]The total flow of the intelligent network bus when more than two intelligent network buses are parked at the bus station in the time period; />Represents [ t, t+Δt ]]The total flow of the intelligent network bus when the bus stops at a time period and is not more than two intelligent network buses; />Represents [ t, t+Δt ]]The total service time of the intelligent network bus at the bus station in the time period comprises the running time from the intersection to the bus station and the time for waiting for passengers to get on or off the bus station; />Represents [ t, t+Δt ]]The running time of the intelligent network bus from the intersection to the bus station in the time period; t represents the current time; deltat represents a unit time step;
wherein, the probability that the bus station stops has more than two intelligent network connection buses and is less than two intelligent network connection buses respectively is:
in the formula ,representing the service strength of a bus station; />Representing the maximum service intensity of a bus station; />Representing the arrival rate of the intelligent network bus; />Representing the service rate of a bus stop->p t (N b =0) indicates the probability that no intelligent network bus is parked in the bus station at time t; j represents the number of intelligent network buses parked in the bus station; k represents the number of intelligent network buses which can reach a bus station;
s3: acquiring the vehicle speed delay time in the running delay time of the mixed traffic flow, and acquiring the signal lamp delay time in the running delay time of the mixed traffic flow according to the average headway of the mixed traffic flow so as to acquire the uniform traffic time of the mixed traffic flow taking the running delay time into consideration;
the running delay time in the embodiment includes a signal lamp delay time and a vehicle speed delay time;
the signal lamp delay time is the driving delay time of the mixed traffic flow at the intersection due to the signal lamp, and the vehicle speed delay time is the driving delay time due to the speed change of the intelligent network private car and the intelligent network bus;
preferably, the signal delay time is obtained as follows:
in the formula ,represents [ y, y+Δy ]]The driving delay time caused by the signal lamp in the time period; r represents the red light duration of the signal light; />Represents [ t, y+Δt ]]The generation speed of the shock wave caused by the red light in the time period; />Represents [ t, t+Δt ]]The dissipation speed of the shock wave after the red light is finished in the time period; c m Representing the maximum flow of the mixed traffic flow at the intersection; />Represents [ t, t+Δt ]]The arrival rate of the mixed traffic flow at the intersection in the time period comprises intelligent network private cars, manual driving cars, left-turning and straight-going intelligent network buses; 1-p RB The proportion of left turn and straight travel of the intelligent network bus is represented; n represents the number of lanes of the road where the mixed traffic flow is located; k (k) j Representing a critical congestion density; />Representing [ t-Deltat, t]The average running speed of the mixed traffic flow in the time period; />Represents [ t, t+Δt ]]The arrival rate of the social vehicles in the time period comprises intelligent network private cars and manual driving vehicles; />Representing the arrival rate of the intelligent network bus;
preferably, the vehicle speed delay time is acquired as follows:
in the formula ,indicating the passing time caused by the speed change of the intelligent network private car and the intelligent network busAn inter-increasing rate; l (L) b Representing the total length of the distance travelled by the bus station to the intersection; l (L) l Representing the total length of a road section where a bus station is located, namely the length between two intersections of the road section where the bus station is located; gamma ray m Representing the proportion of vehicles affected by the speed change of the intelligent network private car and the intelligent network bus; />Represents [ t, t+Δt ]]The expected running speed of the intelligent network private car and the intelligent network bus in the time period;
preferably, the mixed traffic flow average transit time T after the travel delay time is considered t The acquisition is as follows:
s4: according to the ghost density theory, obtaining the average traffic density of the mixed traffic flow;
specifically, the ghost density theory is the prior art, and in this embodiment, the ghost density is the sum of the real vehicle and the ghost vehicle in unit distance; the ghost vehicles are generated under three conditions, namely, the speed change of the intelligent network private car and the intelligent network bus causes the speed reduction of the manual driving vehicle, occupies more time-space domain area and generates ghost vehicles; secondly, the time-space domain in the forced lane change process of the intelligent network bus and the random lane change process of the manual driving vehicle cannot be occupied by other vehicles, so that more time-space domain area is occupied, and ghost vehicles are generated; thirdly, when the intelligent network bus stops at a bus stop, the occupied area in space is unchanged, but more areas are occupied in a time-space domain due to the fact that the intelligent network bus needs to stop for a period of time, and ghost vehicles are generated;
preferably, in the step S4, the average traffic density of the mixed traffic flow is obtained as follows:
in the formula ,represents [ t, t+Δt ]]Traffic density of the mixed traffic stream over a period of time; />Representing [ t-Deltat, t]Total number of vehicles in the mixed traffic stream over a period of time; />Represents [ t, t+Δt ]]The number of ghost vehicles generated in the time period; />Represents [ t, t+Δt ]]Ghost vehicles generated by speed change of intelligent network private vehicles and intelligent network buses in a time period;represents [ t, t+Δt ]]Ghost vehicles generated by intelligent network bus station parking in the time period; />Represents [ t, t+Δt ]]Ghost vehicles generated by forced lane change of intelligent network bus and random lane change of manual driving vehicles in the time period;represents [ t, t+Δt ]]The lane changing distance required by the intelligent network bus in the time period; />Represents [ t, t+Δt ]]Lane changing distance required by manual driving of the vehicle in the time period; />Represents [ t, t+Δt ]]The intelligent network bus stops waiting for passengers to get on or off the bus within the time period; n (N) LC Representing the number of lane change vehicles; t is t LC Indicating the channel change duration; p is p RB The right turn proportion of the intelligent network bus at the intersection is shown; p is p LB Representing the left-turning proportion of the intelligent network bus at the intersection; p is p SB Representing the proportion of the intelligent network bus in straight running at the intersection; n represents the number of lanes of the road where the mixed traffic flow is located, t blc Indicating the time required by lane change of the intelligent network bus; t is t hlc The time required by the manual driving of the vehicle to change the road is represented; l (L) b Representing the length of an intelligent network bus body; l (L) h Representing the length of the body of the manually driven vehicle;
in the formula ,representing intelligent network allianceThe following distance of the bus; />Representing the distance required by acceleration and deceleration of the vehicle in the course of lane change; />Representing the distance required by speed change in the lane change process of the intelligent network bus; t is t b Representing the following time of a manually driven vehicle; />Representing [ t-Deltat, t]Average travel speed of the mixed traffic stream over a period of time; />Representing a following distance of the manually driven vehicle; v l Representing a following speed of the manually driven vehicle; />Representing the distance required by speed change in the course of changing the lane of the manual driving vehicle; d, d f,b Representing the safe following distance of the intelligent network bus; d, d f,h Representing a safe following distance of the manually driven vehicle; τ f,b Indicating the reaction time required by the intelligent network bus; τ f,h Representing the reaction time required by manual driving of the vehicle; />Represents [ t, t+Δt ]]The expected running speeds of the intelligent network bus and the intelligent network private car in the time period;
s5: acquiring the traffic capacity of the mixed traffic flow according to the flow of the intelligent network bus, the uniform traffic flow time of the mixed traffic flow and the average traffic density of the mixed traffic flow and a system dynamic equation; the traffic state can be accurately estimated by means of information technology by estimating the traffic capacity of the mixed traffic flow.
in the formula ：qt Representing the overall traffic of the mixed traffic flow;representing the traffic of social vehicles except intelligent network buses.
The method for calculating the traffic capacity of the mixed traffic flow considering the intelligent network bus provided by the embodiment can be compared with the simulation result to verify the accuracy, and the method can be used for accurately calculating the traffic capacity of the mixed traffic flow by testing the traffic capacity under different congestion conditions and different intelligent network bus permeability conditions. Under various complex actual traffic scenes, the calculation accuracy is more than 85%, as shown in fig. 2.
In summary, the method for calculating the mixed traffic flow capacity of the intelligent network bus is provided by the embodiment of the invention, the bus operation rule (signal lamp) and the heterogeneous vehicle interaction relation are fused, and the influence of the formation condition of a fleet, the average headway, the following and lane change driving behaviors on the mixed traffic flow is explored; the method comprises the steps of providing a calculation method of traffic density of a mixed traffic flow according to a ghost density theory by considering the influence of driving behaviors such as lane changing, speed change and bus stop service on a time-space domain; further, the influence process of the microscopic driving behavior of the vehicle on the macroscopic traffic characteristics is described by combining the driving delay time generated in the process of entering and exiting the bus station and steering at the intersection; as shown in fig. 2, the horizontal axis represents input flow, represents different traffic states, the vertical axis represents error rate of the calculation result of the method in this embodiment compared with the simulation result, and different types of line segments represent different intelligent network link permeability; as can be seen from the figure, compared with simulation results, the method of the embodiment can ensure the calculation accuracy of more than 85% under the conditions of various traffic states and different intelligent network coupling permeabilities, and the solution accuracy is continuously increased along with the increase of the intelligent network coupling permeabilities, so that the method is suitable for various practical application scenes and has strong universality. The method fully considers the difference of heterogeneous vehicle driving behaviors and the complexity of the interaction process, realizes the accurate calculation of the mixed traffic circulation capacity according to the system dynamic equation, and can be effectively applicable to various traffic states and different intelligent network technology development conditions.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention.
Claims (8)
1. The method for calculating the mixed traffic flow traffic capacity of the intelligent network bus is characterized by comprising the following steps of:
s1: acquiring average headway of mixed traffic flows under different intelligent network coupling permeability;
the mixed traffic flow consists of a manual driving vehicle, an intelligent network private car and an intelligent network bus; the intelligent network private car and the intelligent network bus have a smaller time-to-time distance than that of a manual driving car;
the intelligent network connection permeability refers to the proportion of the number of intelligent network connection private cars to the total number of vehicles in the mixed traffic flow;
s2: acquiring the flow of an intelligent network bus running from an intersection to a bus station in the [ t, t+delta t ] time period;
s3: acquiring the vehicle speed delay time in the running delay time of the mixed traffic flow, and acquiring the signal lamp delay time in the running delay time of the mixed traffic flow according to the average headway of the mixed traffic flow so as to acquire the uniform traffic time of the mixed traffic flow taking the running delay time into consideration;
s4: according to the ghost density theory, obtaining the average traffic density of the mixed traffic flow;
s5: and acquiring the traffic of the mixed traffic flow according to the traffic of the intelligent network bus, the equal traffic time of the mixed traffic flow and the average traffic density of the mixed traffic flow so as to evaluate the traffic capacity of the mixed traffic flow.
2. The method for calculating the traffic flow capacity of the intelligent network bus according to claim 1, wherein in S1, the average headway under different intelligent network bus permeabilities is obtained as follows:
in the formula ,representing the average headway of the mixed traffic flow; zeta type toy a Indicating the permeability of intelligent network connection; n is n m Representing a total number of vehicles in the mixed traffic stream; n is n b Indicating the number of intelligent network buses in the mixed traffic flow, and xi a n m Representing the number of intelligent network private cars in the mixed traffic flow; h is a hv-hv Representing a headway between the manually driven vehicle and the manually driven vehicle; h is a hv-cv Representing the headway between a manual driving vehicle and an intelligent network private car; h is a hv-cb Representing the time interval between a manual driving vehicle and an intelligent network bus; h is a cv-hv Representing the headway between the intelligent network private car and the manual driving car; h is a cv-cv Representing the headway between the private car of the intelligent network and the private car of the intelligent network；h cb-hv And the time interval between the intelligent network bus and the manual driving vehicle is represented.
3. The method for calculating the traffic flow capacity of the intelligent network bus according to claim 1, wherein in S2, the flow of the intelligent network bus is obtained as follows:
in the formula ,represents [ t, t+Δt ]]The flow of the intelligent network bus output by the bus station in the time period; n (N) b Representing the number of intelligent network buses in a bus station; p is p r (N b Not less than 2) represents [ t, t+Δt ]]Probability that more than two intelligent network buses stop at a bus stop in a time period; p is p t (N b <2) Represents [ t, t+Δt ]]Probability of stopping less than two intelligent network buses in a time period bus station;represents [ t, t+Δt ]]The total flow of the intelligent network bus when more than two intelligent network buses are parked at the bus station in the time period; />Represents [ t, t+Δt ]]The total flow of the intelligent network bus when the bus stops at a time period and is not more than two intelligent network buses; />Represents [ t, t+Δt ]]The total service time of the intelligent network bus at the bus station in the time period; />Represents [ t, t+Δt ]]The running time of the intelligent network bus from the intersection to the bus station in the time period; t represents the current time; Δt represents a unit time step;
wherein ,
in the formula ,representing the service strength of a bus station; />Representing the maximum service intensity of a bus station; />Representing the arrival rate of the intelligent network bus; />Representing the service rate of a bus stop->p t (N b =0) indicates the probability that no intelligent network bus is parked in the bus station at time t; j represents the number of intelligent network buses parked in the bus station; k represents the number of intelligent network buses that may reach a bus station.
4. The method for calculating the traffic flow capacity of the intelligent network bus according to claim 1, wherein the signal lamp delay time is obtained as follows:
in the formula ,represents [ t, t+Δt ]]The driving delay time caused by the signal lamp in the time period; r represents the red light duration of the signal light; />Represents [ t, t+Δt ]]The generation speed of the shock wave caused by the red light in the time period; />Represents [ t, t+Δt ]]The dissipation speed of the shock wave after the red light is finished in the time period; c m Representing the maximum flow of the mixed traffic flow at the intersection; />Represents [ t, t+Δt ]]The arrival rate of the mixed traffic flow at the intersection in the time period comprises intelligent network private cars, manual driving cars, left-turning and straight-going intelligent network buses; 1-p RB The proportion of left turn and straight travel of the intelligent network bus is represented; n represents the number of lanes of the road where the mixed traffic flow is located; k (k) j Representing a critical congestion density; />Representing [ t-Deltat, t]The average running speed of the mixed traffic flow in the time period; />Represents [ t, t+Δt ]]The arrival rate of the social vehicles in the time period comprises intelligent network private cars and manual driving vehicles; />And the arrival rate of the intelligent network bus is represented.
5. The method for calculating the traffic flow capacity of the intelligent network bus according to claim 1, wherein the vehicle speed delay time is obtained as follows:
in the formula ,representing the passing time increasing rate caused by the speed change of the intelligent network private car and the intelligent network bus; l (L) b Representation ofTotal length of distance travelled by the bus station to the intersection; l (L) l Representing the total length of the road section where the bus station is located; gamma ray m Representing the proportion of vehicles affected by the speed change of the intelligent network private car and the intelligent network bus; />Represents [ t, t+Δt ]]And the expected running speed of the intelligent network private car and the intelligent network bus in the time period.
6. The method for calculating the traffic flow capacity of the intelligent network bus according to claim 1, wherein the traffic flow average traffic time T of the intelligent network bus is calculated by taking the traffic flow after the running delay time into consideration t The acquisition is as follows:
7. the method for calculating the traffic flow capacity of the intelligent network bus according to claim 1, wherein in S4, the average traffic density of the traffic flow is obtained as follows:
in the formula ,represents [ t, t+Δt ]]Traffic density of the mixed traffic stream over a period of time; />Representing [ t-Deltat, t]Total number of vehicles in the mixed traffic stream over a period of time; />Represents [ y, y+Δy ]]The number of ghost vehicles generated in the time period; />Represents [ t, t+Δt ]]Ghost vehicles generated by speed change of intelligent network private vehicles and intelligent network buses in a time period; />Represents [ t, t+Δt ]]Ghost vehicles generated by intelligent network bus station parking in the time period; />Represents [ t, t+Δt ]]Ghost vehicles generated by forced lane change of intelligent network bus and random lane change of manual driving vehicles in the time period; />Represents [ t, t+Δt ]]The lane changing distance required by the intelligent network bus in the time period; />Represents [ t, t+Δt ]]Lane changing distance required by manual driving of the vehicle in the time period; />Represents [ t, t+Δt ]]The intelligent network bus stops waiting for passengers to get on or off the bus within the time period; n (N) LC Representing the number of lane change vehicles; t is t LC Indicating the channel change duration; p is p RB The right turn proportion of the intelligent network bus at the intersection is shown; p is p LB Representing the left-turning proportion of the intelligent network bus at the intersection; p is p SB Representing the proportion of the intelligent network bus in straight running at the intersection; n represents the number of lanes of the road where the mixed traffic flow is located, t blc Indicating the time required by lane change of the intelligent network bus; t is t hlc The time required by the manual driving of the vehicle to change the road is represented; l (L) b Representing the length of an intelligent network bus body; l (L) h Representing the length of the body of the manually driven vehicle;
in the formula ,representing the following distance of the intelligent network bus; />Representing the distance required by acceleration and deceleration of the vehicle in the course of lane change; />Representing the distance required by speed change in the lane change process of the intelligent network bus; t is t b Representing the following time of a manually driven vehicle; />Representing [ t-Deltat, t]Average travel speed of the mixed traffic stream over a period of time; />Representing a following distance of the manually driven vehicle; v l Representing a following speed of the manually driven vehicle; />Representing the distance required by speed change in the course of changing the lane of the manual driving vehicle; d, d f,b Representing the safe following distance of the intelligent network bus; d, d f,h Representing a safe following distance of the manually driven vehicle; τ f,b Indicating the reaction time required by the intelligent network bus; τ f,h Representing the reaction time required by manual driving of the vehicle; />Represents [ t, t+Δt ]]And the expected running speeds of the intelligent Internet-connected buses and the intelligent Internet-connected private buses are within a time period.
8. The method for calculating the traffic capacity of the mixed traffic flow considering the intelligent network bus according to claim 1, wherein the traffic capacity of the mixed traffic flow is obtained as follows:
in the formula ：qt Representing the overall traffic of the mixed traffic flow;representing the traffic of social vehicles except intelligent network buses.
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